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ORCID1st LIDTA 2017: Skopje, Macedonia
- First International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA@PKDD/ECML 2017, 22 September 2017, Skopje, Macedonia. Proceedings of Machine Learning Research 74, PMLR 2017
Preface
- Luís Torgo, Bartosz Krawczyk, Paula Branco, Nuno Moniz:
Learning with Imbalanced Domains: Preface. 1-6
Full Papers
- Przemyslaw Skryjomski, Bartosz Krawczyk:
Influence of minority class instance types on SMOTE imbalanced data oversampling. 7-21 - Piotr Szymanski, Tomasz Kajdanowicz:
A Network Perspective on Stratification of Multi-Label Data. 22-35 - Paula Branco, Luís Torgo, Rita P. Ribeiro:
SMOGN: a Pre-processing Approach for Imbalanced Regression. 36-50 - Arjun Pakrashi
, Brian Mac Namee:
Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest Neighbours. 51-63 - Colin Bellinger, Shiven Sharma, Osmar R. Zaïane, Nathalie Japkowicz:
Sampling a Longer Life: Binary versus One-class classification Revisited. 64-78 - Bing Zhu, Seppe vanden Broucke, Bart Baesens, Sebastián Maldonado:
Improving Resampling-based Ensemble in Churn Prediction. 79-91 - Nikou Günnemann, Jürgen Pfeffer:
Predicting Defective Engines using Convolutional Neural Networks on Temporal Vibration Signals. 92-102 - Xia Cui, Frans Coenen, Danushka Bollegala:
Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection Strategies. 103-115
Posters
- Emmanouil Krasanakis, Eleftherios Spyromitros Xioufis, Symeon Papadopoulos, Yiannis Kompatsiaris:
Tunable Plug-In Rules with Reduced Posterior Certainty Loss in Imbalanced Datasets. 116-128 - Nuno Moniz, Paula Branco, Luís Torgo:
Evaluation of Ensemble Methods in Imbalanced Regression Tasks. 129-140 - Yehezkel S. Resheff, Amit Mandelbom, Daphna Weinshall:
Controlling Imbalanced Error in Deep Learning with the Log Bilinear Loss. 141-151 - Cédric Fayet, Arnaud Delhay, Damien Lolive, Pierre-François Marteau:
Unsupervised Classification of Speaker Profiles as a Point Anomaly Detection Task. 152-163 - Pawel Ksieniewicz, Michal Wozniak:
Dealing with the task of imbalanced, multidimensional data classification using ensembles of exposers. 164-175
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